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Unsupervised image-set clustering using an information theoretic framework.

Jacob Goldberger1, Shiri Gordon, Hayit Greenspan

  • 1Engineering Department, Bar-Ilan University, Ramat-Gan 52900, Israel. goldbej@eng.biu.ac.il

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 17, 2006
PubMed
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This study introduces a novel unsupervised hierarchical image-set clustering method using information theory. The approach enhances image search and retrieval by preserving mutual information between clusters and image content.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Information Theory

Background:

  • Unsupervised clustering is crucial for organizing large image datasets.
  • Existing methods often struggle with complex image-set structures.
  • Information-theoretic principles offer a robust framework for data analysis.

Purpose of the Study:

  • To develop an unsupervised hierarchical image-set clustering framework.
  • To leverage information-theoretic criteria for optimal cluster formation.
  • To improve efficiency in image search and retrieval applications.

Main Methods:

  • Combines discrete and continuous image models.
  • Utilizes a generalized information bottleneck principle.
  • Employs mixture of Gaussian densities for continuous image modeling.

Related Experiment Videos

  • Maximizes mutual information between clusters and image content.

    • Main Results:

    • Demonstrates effective image clustering performance on a large image set.
    • Information theoretic tools validate the quality of the generated clusters.
    • The proposed framework shows promise for efficient image search and retrieval.

    Conclusions:

    • The integrated approach provides a powerful tool for unsupervised image-set clustering.
    • Preserving mutual information is key to effective clustering for retrieval tasks.
    • This method offers a significant advancement in organizing and accessing large image collections.